Overview

Dataset statistics

Number of variables19
Number of observations4000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory622.2 B

Variable types

Text2
Categorical8
Numeric9

Alerts

title has unique valuesUnique
text has unique valuesUnique
trust_score has 44 (1.1%) zerosZeros

Reproduction

Analysis started2026-01-15 12:17:12.275431
Analysis finished2026-01-15 12:17:28.607815
Duration16.33 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

title
Text

Unique 

Distinct4000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size292.0 KiB
2026-01-15T17:17:29.030473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.72325
Min length15

Characters and Unicode

Total characters70893
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4000 ?
Unique (%)100.0%

Sample

1st rowBreaking News 1
2nd rowBreaking News 2
3rd rowBreaking News 3
4th rowBreaking News 4
5th rowBreaking News 5
ValueCountFrequency (%)
breaking4000
33.3%
news4000
33.3%
39691
 
< 0.1%
39991
 
< 0.1%
39981
 
< 0.1%
39971
 
< 0.1%
39961
 
< 0.1%
39951
 
< 0.1%
39941
 
< 0.1%
39931
 
< 0.1%
Other values (3992)3992
33.3%
2026-01-15T17:17:29.620021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e8000
 
11.3%
8000
 
11.3%
r4000
 
5.6%
a4000
 
5.6%
k4000
 
5.6%
B4000
 
5.6%
i4000
 
5.6%
n4000
 
5.6%
g4000
 
5.6%
N4000
 
5.6%
Other values (12)22893
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)70893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8000
 
11.3%
8000
 
11.3%
r4000
 
5.6%
a4000
 
5.6%
k4000
 
5.6%
B4000
 
5.6%
i4000
 
5.6%
n4000
 
5.6%
g4000
 
5.6%
N4000
 
5.6%
Other values (12)22893
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8000
 
11.3%
8000
 
11.3%
r4000
 
5.6%
a4000
 
5.6%
k4000
 
5.6%
B4000
 
5.6%
i4000
 
5.6%
n4000
 
5.6%
g4000
 
5.6%
N4000
 
5.6%
Other values (12)22893
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8000
 
11.3%
8000
 
11.3%
r4000
 
5.6%
a4000
 
5.6%
k4000
 
5.6%
B4000
 
5.6%
i4000
 
5.6%
n4000
 
5.6%
g4000
 
5.6%
N4000
 
5.6%
Other values (12)22893
32.3%

text
Text

Unique 

Distinct4000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size530.3 KiB
2026-01-15T17:17:30.042870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length79
Mean length78.72325
Min length76

Characters and Unicode

Total characters314893
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4000 ?
Unique (%)100.0%

Sample

1st rowThis is the content of article 1. It contains detailed analysis and reports.
2nd rowThis is the content of article 2. It contains detailed analysis and reports.
3rd rowThis is the content of article 3. It contains detailed analysis and reports.
4th rowThis is the content of article 4. It contains detailed analysis and reports.
5th rowThis is the content of article 5. It contains detailed analysis and reports.
ValueCountFrequency (%)
article4000
 
7.7%
detailed4000
 
7.7%
reports4000
 
7.7%
it4000
 
7.7%
this4000
 
7.7%
is4000
 
7.7%
the4000
 
7.7%
content4000
 
7.7%
of4000
 
7.7%
contains4000
 
7.7%
Other values (4002)12000
23.1%
2026-01-15T17:17:30.585242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48000
15.2%
t32000
10.2%
s24000
 
7.6%
e24000
 
7.6%
i24000
 
7.6%
a24000
 
7.6%
n24000
 
7.6%
o16000
 
5.1%
c12000
 
3.8%
r12000
 
3.8%
Other values (19)74893
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)314893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
48000
15.2%
t32000
10.2%
s24000
 
7.6%
e24000
 
7.6%
i24000
 
7.6%
a24000
 
7.6%
n24000
 
7.6%
o16000
 
5.1%
c12000
 
3.8%
r12000
 
3.8%
Other values (19)74893
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)314893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
48000
15.2%
t32000
10.2%
s24000
 
7.6%
e24000
 
7.6%
i24000
 
7.6%
a24000
 
7.6%
n24000
 
7.6%
o16000
 
5.1%
c12000
 
3.8%
r12000
 
3.8%
Other values (19)74893
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)314893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
48000
15.2%
t32000
10.2%
s24000
 
7.6%
e24000
 
7.6%
i24000
 
7.6%
a24000
 
7.6%
n24000
 
7.6%
o16000
 
5.1%
c12000
 
3.8%
r12000
 
3.8%
Other values (19)74893
23.8%

state
Categorical

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size256.5 KiB
Washington
 
225
California
 
225
Florida
 
220
Pennsylvania
 
219
Massachusetts
 
215
Other values (15)
2896 

Length

Max length14
Median length12
Mean length8.63825
Min length4

Characters and Unicode

Total characters34553
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTennessee
2nd rowWisconsin
3rd rowMissouri
4th rowNorth Carolina
5th rowCalifornia

Common Values

ValueCountFrequency (%)
Washington225
 
5.6%
California225
 
5.6%
Florida220
 
5.5%
Pennsylvania219
 
5.5%
Massachusetts215
 
5.4%
Indiana206
 
5.1%
Maryland203
 
5.1%
Wisconsin203
 
5.1%
Illinois199
 
5.0%
Ohio199
 
5.0%
Other values (10)1886
47.1%

Length

2026-01-15T17:17:30.726465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new387
 
8.5%
washington225
 
4.9%
california225
 
4.9%
florida220
 
4.8%
pennsylvania219
 
4.8%
massachusetts215
 
4.7%
indiana206
 
4.5%
maryland203
 
4.4%
wisconsin203
 
4.4%
illinois199
 
4.4%
Other values (12)2267
49.6%

Most occurring characters

ValueCountFrequency (%)
i4193
12.1%
a3888
11.3%
n3689
 
10.7%
s3048
 
8.8%
o2370
 
6.9%
e2343
 
6.8%
r2130
 
6.2%
l1447
 
4.2%
h1019
 
2.9%
t837
 
2.4%
Other values (26)9589
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)34553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i4193
12.1%
a3888
11.3%
n3689
 
10.7%
s3048
 
8.8%
o2370
 
6.9%
e2343
 
6.8%
r2130
 
6.2%
l1447
 
4.2%
h1019
 
2.9%
t837
 
2.4%
Other values (26)9589
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i4193
12.1%
a3888
11.3%
n3689
 
10.7%
s3048
 
8.8%
o2370
 
6.9%
e2343
 
6.8%
r2130
 
6.2%
l1447
 
4.2%
h1019
 
2.9%
t837
 
2.4%
Other values (26)9589
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i4193
12.1%
a3888
11.3%
n3689
 
10.7%
s3048
 
8.8%
o2370
 
6.9%
e2343
 
6.8%
r2130
 
6.2%
l1447
 
4.2%
h1019
 
2.9%
t837
 
2.4%
Other values (26)9589
27.8%

category
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size255.8 KiB
Business
724 
Health
695 
Politics
665 
Sports
644 
Technology
639 

Length

Max length13
Median length10
Mean length8.44125
Min length6

Characters and Unicode

Total characters33765
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntertainment
2nd rowTechnology
3rd rowSports
4th rowSports
5th rowTechnology

Common Values

ValueCountFrequency (%)
Business724
18.1%
Health695
17.4%
Politics665
16.6%
Sports644
16.1%
Technology639
16.0%
Entertainment633
15.8%

Length

2026-01-15T17:17:30.876648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:31.040831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
business724
18.1%
health695
17.4%
politics665
16.6%
sports644
16.1%
technology639
16.0%
entertainment633
15.8%

Most occurring characters

ValueCountFrequency (%)
t3903
11.6%
s3481
10.3%
e3324
 
9.8%
n3262
 
9.7%
i2687
 
8.0%
o2587
 
7.7%
l1999
 
5.9%
h1334
 
4.0%
a1328
 
3.9%
c1304
 
3.9%
Other values (12)8556
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)33765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t3903
11.6%
s3481
10.3%
e3324
 
9.8%
n3262
 
9.7%
i2687
 
8.0%
o2587
 
7.7%
l1999
 
5.9%
h1334
 
4.0%
a1328
 
3.9%
c1304
 
3.9%
Other values (12)8556
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)33765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t3903
11.6%
s3481
10.3%
e3324
 
9.8%
n3262
 
9.7%
i2687
 
8.0%
o2587
 
7.7%
l1999
 
5.9%
h1334
 
4.0%
a1328
 
3.9%
c1304
 
3.9%
Other values (12)8556
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)33765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t3903
11.6%
s3481
10.3%
e3324
 
9.8%
n3262
 
9.7%
i2687
 
8.0%
o2587
 
7.7%
l1999
 
5.9%
h1334
 
4.0%
a1328
 
3.9%
c1304
 
3.9%
Other values (12)8556
25.3%

sentiment_score
Real number (ℝ)

Distinct201
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.000645
Minimum-1
Maximum1
Zeros20
Zeros (%)0.5%
Negative2005
Negative (%)50.1%
Memory size31.4 KiB
2026-01-15T17:17:31.243684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.9
Q1-0.49
median-0.01
Q30.51
95-th percentile0.89
Maximum1
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5747681
Coefficient of variation (CV)-891.11334
Kurtosis-1.1930524
Mean-0.000645
Median Absolute Deviation (MAD)0.5
Skewness-0.0043120571
Sum-2.58
Variance0.33035837
MonotonicityNot monotonic
2026-01-15T17:17:31.452857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6434
 
0.9%
-0.3130
 
0.8%
0.1230
 
0.8%
-0.0130
 
0.8%
0.2529
 
0.7%
0.6729
 
0.7%
-0.8428
 
0.7%
0.8728
 
0.7%
0.1328
 
0.7%
-0.228
 
0.7%
Other values (191)3706
92.7%
ValueCountFrequency (%)
-111
0.3%
-0.9925
0.6%
-0.9818
0.4%
-0.9716
0.4%
-0.9623
0.6%
-0.9524
0.6%
-0.9419
0.5%
-0.9316
0.4%
-0.9221
0.5%
-0.9119
0.5%
ValueCountFrequency (%)
19
 
0.2%
0.9918
0.4%
0.9817
0.4%
0.9713
0.3%
0.9619
0.5%
0.9522
0.5%
0.9425
0.6%
0.9314
0.4%
0.9212
0.3%
0.9126
0.7%

word_count
Real number (ℝ)

Distinct1324
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean795.65575
Minimum100
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:31.639231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile163
Q1445.75
median793
Q31150
95-th percentile1429
Maximum1500
Range1400
Interquartile range (IQR)704.25

Descriptive statistics

Standard deviation406.37387
Coefficient of variation (CV)0.51074082
Kurtosis-1.2085476
Mean795.65575
Median Absolute Deviation (MAD)352
Skewness0.0054378973
Sum3182623
Variance165139.72
MonotonicityNot monotonic
2026-01-15T17:17:31.815528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10410
 
0.2%
93010
 
0.2%
5499
 
0.2%
3159
 
0.2%
13188
 
0.2%
11998
 
0.2%
4808
 
0.2%
8788
 
0.2%
11178
 
0.2%
1548
 
0.2%
Other values (1314)3914
97.9%
ValueCountFrequency (%)
1004
 
0.1%
1012
 
0.1%
1022
 
0.1%
1033
 
0.1%
10410
0.2%
1052
 
0.1%
1065
0.1%
1072
 
0.1%
1082
 
0.1%
1092
 
0.1%
ValueCountFrequency (%)
15004
0.1%
14982
 
0.1%
14974
0.1%
14966
0.1%
14952
 
0.1%
14943
0.1%
14933
0.1%
14926
0.1%
14903
0.1%
14892
 
0.1%

has_images
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size226.7 KiB
0
2014 
1
1986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
02014
50.3%
11986
49.6%

Length

2026-01-15T17:17:32.006917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:32.135546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02014
50.3%
11986
49.6%

Most occurring characters

ValueCountFrequency (%)
02014
50.3%
11986
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02014
50.3%
11986
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02014
50.3%
11986
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02014
50.3%
11986
49.6%

has_videos
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size226.7 KiB
0
2062 
1
1938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02062
51.5%
11938
48.4%

Length

2026-01-15T17:17:32.276108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:32.376372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02062
51.5%
11938
48.4%

Most occurring characters

ValueCountFrequency (%)
02062
51.5%
11938
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02062
51.5%
11938
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02062
51.5%
11938
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02062
51.5%
11938
48.4%

readability_score
Real number (ℝ)

Distinct2734
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.764595
Minimum30.02
Maximum79.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:32.517475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30.02
5-th percentile32.8695
Q142.48
median54.235
Q367.215
95-th percentile77.5005
Maximum79.98
Range49.96
Interquartile range (IQR)24.735

Descriptive statistics

Standard deviation14.404027
Coefficient of variation (CV)0.26301713
Kurtosis-1.2047308
Mean54.764595
Median Absolute Deviation (MAD)12.395
Skewness0.046218813
Sum219058.38
Variance207.47598
MonotonicityNot monotonic
2026-01-15T17:17:32.705032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.955
 
0.1%
67.615
 
0.1%
46.175
 
0.1%
74.225
 
0.1%
38.125
 
0.1%
51.795
 
0.1%
33.275
 
0.1%
55.554
 
0.1%
54.364
 
0.1%
69.924
 
0.1%
Other values (2724)3953
98.8%
ValueCountFrequency (%)
30.021
 
< 0.1%
30.033
0.1%
30.042
0.1%
30.051
 
< 0.1%
30.091
 
< 0.1%
30.11
 
< 0.1%
30.111
 
< 0.1%
30.121
 
< 0.1%
30.131
 
< 0.1%
30.142
0.1%
ValueCountFrequency (%)
79.981
 
< 0.1%
79.972
0.1%
79.951
 
< 0.1%
79.934
0.1%
79.921
 
< 0.1%
79.912
0.1%
79.93
0.1%
79.891
 
< 0.1%
79.832
0.1%
79.81
 
< 0.1%

num_shares
Real number (ℝ)

Distinct3849
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25144.597
Minimum39
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:32.884818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile2504.8
Q112781.75
median25308.5
Q337453.5
95-th percentile47642.15
Maximum50000
Range49961
Interquartile range (IQR)24671.75

Descriptive statistics

Standard deviation14387.537
Coefficient of variation (CV)0.57219201
Kurtosis-1.1935124
Mean25144.597
Median Absolute Deviation (MAD)12319
Skewness-0.015970686
Sum1.0057839 × 108
Variance2.0700123 × 108
MonotonicityNot monotonic
2026-01-15T17:17:33.063540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14783
 
0.1%
160193
 
0.1%
288613
 
0.1%
78863
 
0.1%
365002
 
0.1%
237842
 
0.1%
200742
 
0.1%
471612
 
0.1%
359552
 
0.1%
84612
 
0.1%
Other values (3839)3976
99.4%
ValueCountFrequency (%)
391
< 0.1%
451
< 0.1%
681
< 0.1%
841
< 0.1%
981
< 0.1%
1351
< 0.1%
1692
0.1%
1751
< 0.1%
1831
< 0.1%
1871
< 0.1%
ValueCountFrequency (%)
500001
< 0.1%
499871
< 0.1%
499811
< 0.1%
499761
< 0.1%
499621
< 0.1%
499491
< 0.1%
499471
< 0.1%
499361
< 0.1%
499321
< 0.1%
499181
< 0.1%

num_comments
Real number (ℝ)

Distinct982
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489.87025
Minimum0
Maximum1000
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:33.455683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1238
median483
Q3741
95-th percentile947
Maximum1000
Range1000
Interquartile range (IQR)503

Descriptive statistics

Standard deviation287.43573
Coefficient of variation (CV)0.58675891
Kurtosis-1.1930491
Mean489.87025
Median Absolute Deviation (MAD)250
Skewness0.051458798
Sum1959481
Variance82619.301
MonotonicityNot monotonic
2026-01-15T17:17:33.632521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63312
 
0.3%
34911
 
0.3%
52611
 
0.3%
16911
 
0.3%
92610
 
0.2%
31910
 
0.2%
80710
 
0.2%
85910
 
0.2%
3310
 
0.2%
38710
 
0.2%
Other values (972)3895
97.4%
ValueCountFrequency (%)
05
0.1%
14
0.1%
26
0.1%
34
0.1%
47
0.2%
54
0.1%
67
0.2%
72
 
0.1%
82
 
0.1%
93
0.1%
ValueCountFrequency (%)
10005
0.1%
9993
 
0.1%
9982
 
0.1%
9977
0.2%
9965
0.1%
9958
0.2%
9942
 
0.1%
9933
 
0.1%
9923
 
0.1%
9915
0.1%

political_bias
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size242.3 KiB
Left
1357 
Center
1325 
Right
1318 

Length

Max length6
Median length5
Mean length4.992
Min length4

Characters and Unicode

Total characters19968
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCenter
2nd rowLeft
3rd rowCenter
4th rowCenter
5th rowRight

Common Values

ValueCountFrequency (%)
Left1357
33.9%
Center1325
33.1%
Right1318
33.0%

Length

2026-01-15T17:17:33.810354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:33.906179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
left1357
33.9%
center1325
33.1%
right1318
33.0%

Most occurring characters

ValueCountFrequency (%)
e4007
20.1%
t4000
20.0%
L1357
 
6.8%
f1357
 
6.8%
C1325
 
6.6%
n1325
 
6.6%
r1325
 
6.6%
R1318
 
6.6%
i1318
 
6.6%
g1318
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)19968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4007
20.1%
t4000
20.0%
L1357
 
6.8%
f1357
 
6.8%
C1325
 
6.6%
n1325
 
6.6%
r1325
 
6.6%
R1318
 
6.6%
i1318
 
6.6%
g1318
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4007
20.1%
t4000
20.0%
L1357
 
6.8%
f1357
 
6.8%
C1325
 
6.6%
n1325
 
6.6%
r1325
 
6.6%
R1318
 
6.6%
i1318
 
6.6%
g1318
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4007
20.1%
t4000
20.0%
L1357
 
6.8%
f1357
 
6.8%
C1325
 
6.6%
n1325
 
6.6%
r1325
 
6.6%
R1318
 
6.6%
i1318
 
6.6%
g1318
 
6.6%

fact_check_rating
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size241.1 KiB
Mixed
1372 
FALSE
1344 
TRUE
1284 

Length

Max length5
Median length5
Mean length4.679
Min length4

Characters and Unicode

Total characters18716
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFALSE
2nd rowMixed
3rd rowMixed
4th rowTRUE
5th rowMixed

Common Values

ValueCountFrequency (%)
Mixed1372
34.3%
FALSE1344
33.6%
TRUE1284
32.1%

Length

2026-01-15T17:17:34.033999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:34.129998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mixed1372
34.3%
false1344
33.6%
true1284
32.1%

Most occurring characters

ValueCountFrequency (%)
E2628
14.0%
i1372
 
7.3%
x1372
 
7.3%
e1372
 
7.3%
M1372
 
7.3%
d1372
 
7.3%
F1344
 
7.2%
L1344
 
7.2%
A1344
 
7.2%
S1344
 
7.2%
Other values (3)3852
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)18716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E2628
14.0%
i1372
 
7.3%
x1372
 
7.3%
e1372
 
7.3%
M1372
 
7.3%
d1372
 
7.3%
F1344
 
7.2%
L1344
 
7.2%
A1344
 
7.2%
S1344
 
7.2%
Other values (3)3852
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E2628
14.0%
i1372
 
7.3%
x1372
 
7.3%
e1372
 
7.3%
M1372
 
7.3%
d1372
 
7.3%
F1344
 
7.2%
L1344
 
7.2%
A1344
 
7.2%
S1344
 
7.2%
Other values (3)3852
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E2628
14.0%
i1372
 
7.3%
x1372
 
7.3%
e1372
 
7.3%
M1372
 
7.3%
d1372
 
7.3%
F1344
 
7.2%
L1344
 
7.2%
A1344
 
7.2%
S1344
 
7.2%
Other values (3)3852
20.6%

is_satirical
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size226.7 KiB
0
2012 
1
1988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02012
50.3%
11988
49.7%

Length

2026-01-15T17:17:34.257117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:34.360544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02012
50.3%
11988
49.7%

Most occurring characters

ValueCountFrequency (%)
02012
50.3%
11988
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02012
50.3%
11988
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02012
50.3%
11988
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02012
50.3%
11988
49.7%

trust_score
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.96075
Minimum0
Maximum100
Zeros44
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:34.498895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median50
Q376
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.467911
Coefficient of variation (CV)0.58982124
Kurtosis-1.2203769
Mean49.96075
Median Absolute Deviation (MAD)26
Skewness0.001527655
Sum199843
Variance868.3578
MonotonicityNot monotonic
2026-01-15T17:17:34.702884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
956
 
1.4%
8055
 
1.4%
2154
 
1.4%
351
 
1.3%
3151
 
1.3%
8450
 
1.2%
10050
 
1.2%
9850
 
1.2%
7149
 
1.2%
5749
 
1.2%
Other values (91)3485
87.1%
ValueCountFrequency (%)
044
1.1%
133
0.8%
243
1.1%
351
1.3%
438
0.9%
547
1.2%
634
0.9%
748
1.2%
831
0.8%
956
1.4%
ValueCountFrequency (%)
10050
1.2%
9940
1.0%
9850
1.2%
9738
0.9%
9640
1.0%
9537
0.9%
9433
0.8%
9334
0.9%
9241
1.0%
9136
0.9%

source_reputation
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.54925
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:34.846795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8742198
Coefficient of variation (CV)0.51794744
Kurtosis-1.2058065
Mean5.54925
Median Absolute Deviation (MAD)2
Skewness-0.041402403
Sum22197
Variance8.2611397
MonotonicityNot monotonic
2026-01-15T17:17:34.957615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8447
11.2%
1425
10.6%
6416
10.4%
10413
10.3%
5402
10.1%
7393
9.8%
4392
9.8%
3385
9.6%
9376
9.4%
2351
8.8%
ValueCountFrequency (%)
1425
10.6%
2351
8.8%
3385
9.6%
4392
9.8%
5402
10.1%
6416
10.4%
7393
9.8%
8447
11.2%
9376
9.4%
10413
10.3%
ValueCountFrequency (%)
10413
10.3%
9376
9.4%
8447
11.2%
7393
9.8%
6416
10.4%
5402
10.1%
4392
9.8%
3385
9.6%
2351
8.8%
1425
10.6%

clickbait_score
Real number (ℝ)

Distinct101
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4944475
Minimum0
Maximum1
Zeros18
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:35.101560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.24
median0.49
Q30.74
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2891375
Coefficient of variation (CV)0.58476886
Kurtosis-1.2008695
Mean0.4944475
Median Absolute Deviation (MAD)0.25
Skewness0.024136733
Sum1977.79
Variance0.083600495
MonotonicityNot monotonic
2026-01-15T17:17:35.322353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4752
 
1.3%
0.6751
 
1.3%
0.1749
 
1.2%
0.2449
 
1.2%
0.4549
 
1.2%
0.0248
 
1.2%
0.1448
 
1.2%
0.8648
 
1.2%
0.5948
 
1.2%
0.8548
 
1.2%
Other values (91)3510
87.8%
ValueCountFrequency (%)
018
 
0.4%
0.0142
1.1%
0.0248
1.2%
0.0340
1.0%
0.0445
1.1%
0.0539
1.0%
0.0636
0.9%
0.0743
1.1%
0.0833
0.8%
0.0947
1.2%
ValueCountFrequency (%)
121
0.5%
0.9939
1.0%
0.9844
1.1%
0.9737
0.9%
0.9635
0.9%
0.9533
0.8%
0.9436
0.9%
0.9336
0.9%
0.9242
1.1%
0.9136
0.9%

plagiarism_score
Real number (ℝ)

Distinct3320
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.59811
Minimum0.04
Maximum99.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2026-01-15T17:17:35.504710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile4.67
Q125.915
median51.48
Q375.58
95-th percentile95.181
Maximum99.95
Range99.91
Interquartile range (IQR)49.665

Descriptive statistics

Standard deviation28.932298
Coefficient of variation (CV)0.5718059
Kurtosis-1.1826577
Mean50.59811
Median Absolute Deviation (MAD)24.79
Skewness-0.046223639
Sum202392.44
Variance837.07787
MonotonicityNot monotonic
2026-01-15T17:17:35.696468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.564
 
0.1%
66.534
 
0.1%
55.554
 
0.1%
45.964
 
0.1%
68.474
 
0.1%
89.944
 
0.1%
7.754
 
0.1%
593
 
0.1%
82.053
 
0.1%
29.543
 
0.1%
Other values (3310)3963
99.1%
ValueCountFrequency (%)
0.041
< 0.1%
0.061
< 0.1%
0.091
< 0.1%
0.12
0.1%
0.131
< 0.1%
0.151
< 0.1%
0.181
< 0.1%
0.212
0.1%
0.221
< 0.1%
0.271
< 0.1%
ValueCountFrequency (%)
99.952
0.1%
99.881
 
< 0.1%
99.822
0.1%
99.811
 
< 0.1%
99.751
 
< 0.1%
99.711
 
< 0.1%
99.682
0.1%
99.653
0.1%
99.641
 
< 0.1%
99.611
 
< 0.1%

label
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
Fake
2026 
Real
1974 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFake
2nd rowFake
3rd rowFake
4th rowFake
5th rowReal

Common Values

ValueCountFrequency (%)
Fake2026
50.6%
Real1974
49.4%

Length

2026-01-15T17:17:35.855979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-15T17:17:35.948704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fake2026
50.6%
real1974
49.4%

Most occurring characters

ValueCountFrequency (%)
a4000
25.0%
e4000
25.0%
F2026
12.7%
k2026
12.7%
R1974
12.3%
l1974
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)16000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a4000
25.0%
e4000
25.0%
F2026
12.7%
k2026
12.7%
R1974
12.3%
l1974
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a4000
25.0%
e4000
25.0%
F2026
12.7%
k2026
12.7%
R1974
12.3%
l1974
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a4000
25.0%
e4000
25.0%
F2026
12.7%
k2026
12.7%
R1974
12.3%
l1974
12.3%

Interactions

2026-01-15T17:17:26.352854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:14.513336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:16.480123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:17.855696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:19.266662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:20.634904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:22.189743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:23.541068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:24.897520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:26.527259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:14.933321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:16.654185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:18.045879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:19.435159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-15T17:17:22.354245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:23.711372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:25.089194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:26.659096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:15.113597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:16.813166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-15T17:17:22.488091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-15T17:17:25.224858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:26.837291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:15.275267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:16.965296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:18.378703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:19.764498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:21.105461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:22.648464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:23.996502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:25.376819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:26.977811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:15.441550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:17.108643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:18.511176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:19.892885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:21.263858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:22.796520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:24.135641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:25.536119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:27.123476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:15.664196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:17.280689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:18.669081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:20.051800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:21.427111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:22.942728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:24.292248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:25.729184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-15T17:17:20.191250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-15T17:17:23.077304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-15T17:17:24.618056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:26.015312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:27.579397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:16.309758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:17.712115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:19.118812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:20.490459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:22.051604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:23.367962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:24.754334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-15T17:17:26.190149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-15T17:17:36.056410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
categoryclickbait_scorefact_check_ratinghas_imageshas_videosis_satiricallabelnum_commentsnum_sharesplagiarism_scorepolitical_biasreadability_scoresentiment_scoresource_reputationstatetrust_scoreword_count
category1.0000.0000.0210.0300.0000.0000.0000.0040.0130.0020.0160.0000.0140.0200.0000.0170.018
clickbait_score0.0001.0000.0000.0000.0070.0170.050-0.0020.0020.0150.000-0.0110.0140.0260.027-0.013-0.035
fact_check_rating0.0210.0001.0000.0000.0000.0000.0000.0150.0000.0000.0170.0000.0000.0320.0000.0230.000
has_images0.0300.0000.0001.0000.0000.0000.0000.0000.0000.0000.0040.0340.0000.0000.0080.0280.000
has_videos0.0000.0070.0000.0001.0000.0070.0220.0000.0000.0070.0050.0000.0000.0410.0370.0000.033
is_satirical0.0000.0170.0000.0000.0071.0000.0000.0690.0000.0200.0000.0060.0000.0000.0000.0000.042
label0.0000.0500.0000.0000.0220.0001.0000.0060.0000.0000.0260.0220.0000.0000.0170.0370.000
num_comments0.004-0.0020.0150.0000.0000.0690.0061.000-0.0030.0250.000-0.026-0.0220.0070.0000.0000.002
num_shares0.0130.0020.0000.0000.0000.0000.000-0.0031.000-0.0060.0330.0080.029-0.0180.0000.0130.002
plagiarism_score0.0020.0150.0000.0000.0070.0200.0000.025-0.0061.0000.0380.0020.000-0.0350.0120.0000.031
political_bias0.0160.0000.0170.0040.0050.0000.0260.0000.0330.0381.0000.0000.0320.0240.0130.0000.000
readability_score0.000-0.0110.0000.0340.0000.0060.022-0.0260.0080.0020.0001.000-0.0020.0150.000-0.0200.013
sentiment_score0.0140.0140.0000.0000.0000.0000.000-0.0220.0290.0000.032-0.0021.0000.0100.0000.003-0.004
source_reputation0.0200.0260.0320.0000.0410.0000.0000.007-0.018-0.0350.0240.0150.0101.0000.0260.004-0.009
state0.0000.0270.0000.0080.0370.0000.0170.0000.0000.0120.0130.0000.0000.0261.0000.0000.000
trust_score0.017-0.0130.0230.0280.0000.0000.0370.0000.0130.0000.000-0.0200.0030.0040.0001.0000.003
word_count0.018-0.0350.0000.0000.0330.0420.0000.0020.0020.0310.0000.013-0.004-0.0090.0000.0031.000

Missing values

2026-01-15T17:17:28.062656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-15T17:17:28.330875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

titletextstatecategorysentiment_scoreword_counthas_imageshas_videosreadability_scorenum_sharesnum_commentspolitical_biasfact_check_ratingis_satiricaltrust_scoresource_reputationclickbait_scoreplagiarism_scorelabel
0Breaking News 1This is the content of article 1. It contains detailed analysis and reports.TennesseeEntertainment-0.2213020066.1847305450CenterFALSE17660.8453.35Fake
1Breaking News 2This is the content of article 2. It contains detailed analysis and reports.WisconsinTechnology0.923221041.1039804530LeftMixed1150.8528.28Fake
2Breaking News 3This is the content of article 3. It contains detailed analysis and reports.MissouriSports0.252280130.0445860763CenterMixed05710.720.38Fake
3Breaking News 4This is the content of article 4. It contains detailed analysis and reports.North CarolinaSports0.941551075.1634222945CenterTRUE118100.9232.20Fake
4Breaking News 5This is the content of article 5. It contains detailed analysis and reports.CaliforniaTechnology-0.019621043.9035934433RightMixed09560.6677.70Real
5Breaking News 6This is the content of article 6. It contains detailed analysis and reports.North CarolinaSports0.839200042.881314828RightFALSE0810.0172.10Fake
6Breaking News 7This is the content of article 7. It contains detailed analysis and reports.MarylandBusiness0.816510162.3913627665CenterMixed01100.4797.59Fake
7Breaking News 8This is the content of article 8. It contains detailed analysis and reports.MarylandPolitics-0.967171075.076035323CenterTRUE17950.5875.33Real
8Breaking News 9This is the content of article 9. It contains detailed analysis and reports.TennesseePolitics-0.6410930073.9349000881CenterTRUE19670.0839.37Fake
9Breaking News 10This is the content of article 10. It contains detailed analysis and reports.MarylandBusiness-0.5014210151.9430508782CenterFALSE18830.6899.12Real
titletextstatecategorysentiment_scoreword_counthas_imageshas_videosreadability_scorenum_sharesnum_commentspolitical_biasfact_check_ratingis_satiricaltrust_scoresource_reputationclickbait_scoreplagiarism_scorelabel
3990Breaking News 3991This is the content of article 3991. It contains detailed analysis and reports.MichiganEntertainment-0.412351135.3425392968CenterFALSE16010.8788.60Real
3991Breaking News 3992This is the content of article 3992. It contains detailed analysis and reports.FloridaTechnology0.4111901134.5223890668RightTRUE19630.1469.46Fake
3992Breaking News 3993This is the content of article 3993. It contains detailed analysis and reports.CaliforniaSports0.2014081157.694792496CenterFALSE17220.6573.27Real
3993Breaking News 3994This is the content of article 3994. It contains detailed analysis and reports.TexasTechnology-0.4510050036.7126287592LeftFALSE0780.9876.95Fake
3994Breaking News 3995This is the content of article 3995. It contains detailed analysis and reports.VirginiaSports-0.264051042.324991456RightMixed17190.3768.40Fake
3995Breaking News 3996This is the content of article 3996. It contains detailed analysis and reports.OhioTechnology0.9112271167.3238880697RightMixed029100.2295.46Fake
3996Breaking News 3997This is the content of article 3997. It contains detailed analysis and reports.WashingtonSports-0.5712960134.863650925LeftFALSE15330.4216.54Fake
3997Breaking News 3998This is the content of article 3998. It contains detailed analysis and reports.CaliforniaEntertainment-0.175220148.2935391577LeftFALSE02290.5028.51Fake
3998Breaking News 3999This is the content of article 3999. It contains detailed analysis and reports.IllinoisHealth-0.881691063.1840424201LeftFALSE1360.1771.16Real
3999Breaking News 4000This is the content of article 4000. It contains detailed analysis and reports.TexasHealth-0.954650071.2448913279RightTRUE17340.0927.65Real